Local Field Potential Frequency Research Articles

Multichannel brain recordings in behavingDrosophilareveal oscillatory activity and local coherence in response to sensory stimulation and circuit activation

Neural networks in vertebrates exhibit endogenous oscillations that have been associated with functions ranging from sensory processing to locomotion. It remains unclear whether oscillations may play a similar role in the insect brain. We describe a novel "whole brain" readout for Drosophila melanogaster using a simple multichannel recording preparation to study electrical activity across the brain of flies exposed to different sensory stimuli. We recorded local field potential (LFP) activity from >2,000 registered recording sites across the fly brain in >200 wild-type and transgenic animals to uncover specific LFP frequency bands that correlate with: 1) brain region; 2) sensory modality (olfactory, visual, or mechanosensory); and 3) activity in specific neural circuits. We found endogenous and stimulus-specific oscillations throughout the fly brain. Central (higher-order) brain regions exhibited sensory modality-specific increases in power within narrow frequency bands. Conversely, in sensory brain regions such as the optic or antennal lobes, LFP coherence, rather than power, best defined sensory responses across modalities. By transiently activating specific circuits via expression of TrpA1, we found that several circuits in the fly brain modulate LFP power and coherence across brain regions and frequency domains. However, activation of a neuromodulatory octopaminergic circuit specifically increased neuronal coherence in the optic lobes during visual stimulation while decreasing coherence in central brain regions. Our multichannel recording and brain registration approach provides an effective way to track activity simultaneously across the fly brain in vivo, allowing investigation of functional roles for oscillations in processing sensory stimuli and modulating behavior.

G.P.85 Cerebellar dysfunction in the mdx mouse: An electrophysiological and behavioral study

Patients with Duchenne muscular dystrophy (DMD), besides a severe muscle impairment caused by the absence of dystrophin expression in skeletal muscle, frequently display specific cognitive deficits. These cognitive deficits have been related to the absence of dystrophin in specific regions of the central nervous system, notably cerebellar Purkinje cells (PC). In the central nervous system, dystrophin has recently been involved in GABAA receptors clustering at postsynaptic densities, and its absence, by disrupting this clustering, leads to decreased inhibitory input to PC in mdx mice. Given these abnormalities, we performed an in vivo electrophysiological study of the dystrophin-deficient mdx mouse, to compare cerebellar PC firing behaviour and local field potential (LFP) in alert mdx and control C57Bl/10 mice. We found an altered PC activity in mdx mice, characterized by a significant increase in simple spikes (SS) firing rate, regularity and rhythmicity. Complex spikes (CS) firing rate and duration were not significantly different between mdx and control mice. However, the duration of the pause induced by CS in SS firing was significantly longer in mdx mice. Furthermore, we demonstrated the presence of high frequency (∼160–200Hz) LFP oscillations in the cerebellar cortex of alert mdx mice. Finally, we compared the performance of mdx and control mice in behavioral tests. While the rotarod test revealed no significant difference in performance between mdx and control mice, the catwalk, runway and wire tests demonstrated a moderate impairment of motor coordination and balance in mdx mice. These findings demonstrate the existence of a cerebellar dysfunction in mdx mice. A similar cerebellar dysfunction may be present in patients with DMD and contribute to the cognitive deficits observed in these patients.

Information coding in a laminar computational model of cat primary visual cortex

Neural populations across cortical layers perform different computational tasks. However, it is not known whether information in different layers is encoded using a common neural code or whether it depends on the specific layer. Here we studied the laminar distribution of information in a large-scale computational model of cat primary visual cortex. We analyzed the amount of information about the input stimulus conveyed by the different representations of the cortical responses. In particular, we compared the information encoded in four possible neural codes: (1) the information carried by the firing rate of individual neurons; (2) the information carried by spike patterns within a time window; (3) the rate-and-phase information carried by the firing rate labelled by the phase of the Local Field Potentials (LFP); (4) the pattern-and-phase information carried by the spike patterns tagged with the LFP phase. We found that there is substantially more information in the rate-and-phase code compared with the firing rate alone for low LFP frequency bands (less than 30Hz). When comparing how information is encoded across layers, we found that the extra information contained in a rate-and-phase code may reach 90% in Layer 4, while in other layers it reaches only 60%, compared to the information carried by the firing rate alone. These results suggest that information processing in primary sensory cortices could rely on different coding strategies across different layers.

Response Properties of Local Field Potentials and Neighboring Single Neurons in Awake Primary Visual Cortex

Recordings from local field potentials (LFPs) are becoming increasingly common in research and clinical applications, but we still have a poor understanding of how LFP stimulus selectivity originates from the combined activity of single neurons. Here, we systematically compared the stimulus selectivity of LFP and neighboring single-unit activity (SUA) recorded in area primary visual cortex (V1) of awake primates. We demonstrate that LFP and SUA have similar stimulus preferences for orientation, direction of motion, contrast, size, temporal frequency, and even spatial phase. However, the average SUA had 50 times better signal-to-noise, 20% higher contrast sensitivity, 45% higher direction selectivity, and 15% more tuning depth than the average LFP. Low LFP frequencies (<30 Hz) were most strongly correlated with the spiking frequencies of neurons with nonlinear spatial summation and poor orientation/direction selectivity that were located near cortical current sinks (negative LFPs). In contrast, LFP gamma frequencies (>30 Hz) were correlated with a more diverse group of neurons located near cortical sources (positive LFPs). In summary, our results indicate that low- and high-frequency LFP pool signals from V1 neurons with similar stimulus preferences but different response properties and cortical depths.

Simplified model of the frequency dependence of the LFP’s spatial reach

One of the fundamental questions regarding the local field potential (LFP), the low-frequency part of the extracellularly recorded electric potential, is how far the signal propagates in the brain [1]. We have previously shown [2] that the low-pass filtering in dendrites [3] leads to a frequency dependent spatial spread of the LFP. These previous results were obtained by simulating a large population of morphologically reconstructed neurons. The cells were placed homogeneously within a disc of radius R = 1mm (Figure ​(Figure1A).1A). We defined the reach of the LFP as a radius r <R such that the cells located beyond that radius contributed no more than 5% of the total amplitude at the center of the population. We showed that the reach depends, among other factors, on the input correlation and the frequency. Figure 1 A. Model setup, r (reach) and R defined in the text. B. The reach r at the soma level as a function of LFP frequency and input correlation for a population of layer 5 pyramidal neurons stimulated basally, symbols – full simulation, lines – ... Here we employ a simplified model of the population to identify the two main effects behin the frequency dependence of the reach: 1) frequency dependence of the ‘transition distance’, that is, the distance beyond which a single cell can be approximated as a dipole, 2) frequency dependence of the mean pairwise correlation of the single neuron contributions to the LFP. The simplified model is in agreement with the full simulation results if both effects are taken into account (Figure ​(Figure1B),1B), while neither of the factors alone is sufficient.

FMRI correlates for low frequency local field potentials appear as a spatiotemporal dynamic under multiple anesthetic conditions

In the previous decade, interest in the “functional connectivity” of the brain has greatly increased, but the nature of the signal underlying derived predictive metrics remains poorly understood [1]. A typical study uses functional magnetic resonance imaging (fMRI) and calculates regions of correlated low-frequency activity or “functional networks” when no task is being performed, the “resting state”. However, unlike traditional block/event based fMRI, the spontaneous fluctuations that determine such networks may not relate to a standard “hemodynamic response” to neural activity [2] and may be task and brain region dependent [1]. Ten rats were anesthetized with either isoflurane (iso) or dexmedetomidine (med). Each rat had simultaneous local field potentials (LFP) [3] recorded from implanted electrodes in bilateral primary somatosensory cortex (SI) simultaneously with single-slice fMRI of SI [4]. After preprocessing, signals were filtered to regions of significant spectral coherence (0.04-0.18Hz iso, 0.05-0.3Hz med). Pearson correlation (rt) was calculated between LFP signals at time shifts -10s to 10s relative to fMRI, at every fMRI voxel (Figure ​(Figure1B).1B). Instead of a simple hemodynamic response, the LFP correlates appeared both to have a component of spatial propagation (Figure ​(Figure1B,1B, white arrows), and alternation between positive and negative correlation. This was observed using both anesthesias and suggests that LFPs in coherent frequencies do not simply reflect local activation, but may instead be part of a large scale dynamic process. Using an fMRI-based algorithm validated in both anesthetized rats and awake humans [5], a spatiotemporal dynamic was produced that was highly similar to rt (Figure ​(Figure1C).1C). Spatial correlation (rs) between the two types of pattern reached a maximum at approximately the same shift between patterns in all rats, mean rs = 0.25 (med) and mean rs = 0.23 (iso), with mean rs > 0.10 indicating significance at p < 0.05 when using boot-strapping and correcting for multiple comparisons [6]. These results suggest that the neural basis of functional networks may be more complex than a simple hemodynamic response and possibly contains contributions from large-scale neuromodulatory processes. Figure 1 A. A coronal image of a rat’s brain in the same plane as the fMRI images used in this study. B. (med) rt between LFP and fMRI at each voxel, times listed are the time shift of LFP prior to fMRI. C. (med) fMRI pattern from Majeed et al. algorithm ...

Relationships between spike-free local field potentials and spike timing in human temporal cortex

Intracortical recordings comprise both fast events, action potentials (APs), and slower events, known as local field potentials (LFPs). Although it is believed that LFPs mostly reflect local synaptic activity, it is unclear which of their signal components are most closely related to synaptic potentials and would therefore be causally related to the occurrence of individual APs. This issue is complicated by the significant contribution from AP waveforms, especially at higher LFP frequencies. In recordings of single-cell activity and LFPs from the human temporal cortex, we computed quantitative, nonlinear, causal dynamic models for the prediction of AP timing from LFPs, at millisecond resolution, before and after removing AP contributions to the LFP. In many cases, the timing of a significant number of single APs could be predicted from spike-free LFPs at different frequencies. Not surprisingly, model performance was superior when spikes were not removed. Cells whose activity was predicted by the spike-free LFP models generally fell into one of two groups: in the first group, neuronal spike activity was associated with specific phases of low LFP frequencies, lower spike activity at high LFP frequencies, and a stronger linear component in the spike-LFP model; in the second group, neuronal spike activity was associated with larger amplitude of high LFP frequencies, less frequent phase locking, and a stronger nonlinear model component. Spike timing in the first group was better predicted by the sign and level of the LFP preceding the spike, whereas spike timing in the second group was better predicted by LFP power during a certain time window before the spike.

Optimal band separation of extracellular field potentials

Local Field Potentials (LFPs) exhibit a broadband spectral structure that is traditionally partitioned into distinct frequency bands which are thought to originate from different types of neural events triggered by different processing pathways. However, the exact frequency boundaries of these processes are not known and, as a result, the frequency bands are often selected based on intuition, previous literature or visual inspection of the data. Here, we address these problems by developing a rigorous method for defining LFP frequency bands and their boundaries. The criterion introduced for determining the boundaries delimiting the bands is to maximize the information about an external correlate carried jointly by all bands in the partition. The method first partitions the LFP frequency range into two bands and then successively increases the number of bands in the partition. We applied the partitioning method to LFPs recorded from primary visual cortex of anaesthetized macaques, and we determined the optimal band partitioning that describes the encoding of naturalistic visual stimuli. The first optimal boundary partitioned the LFP response at 60Hz into low and high frequencies, which had been previously found to convey independent information about the natural movie correlate. The second optimal boundary divided the high-frequency range at approximately 100Hz into gamma and high-gamma frequencies, consistent with recent reports that these two bands reflect partly distinct neural processes. A third important boundary was at 25Hz and it split the LFP range below 50Hz into a stimulus-informative and a stimulus-independent band.

Spatiotemporal Variation of Multiple Neurophysiological Signals in the Primary Motor Cortex during Dexterous Reach-to-Grasp Movements

To examine the spatiotemporal distribution of discriminable information about reach-to-grasp movements in the primary motor cortex upper extremity representation, we implanted four microelectrode arrays in the anterior bank and lip of the central sulcus in each of two monkeys. We used linear discriminant analysis to compare information, quantified as decoding accuracy, contained in various neurophysiological signals. For all signal types, decoding accuracy increased immediately after the movement cue, peaked around movement onset, and declined during the static hold. Spike recordings and local field potential (LFP) time domain amplitude provided more discriminable information than LFP frequency domain power. Discriminable information on movement type was distributed evenly across recording sites by LFP amplitude and 1-4 Hz power but unevenly by 100-170 Hz power and spike recordings. These latter two signal types provided higher decoding accuracies closer to the hemispheric surface than deep in the anterior bank and also provided accuracies that varied along the central sulcus. This variation in the distribution of movement-type information may be related to differences in the rostral versus caudal regions of the primary motor cortex and to its underlying somatotopic organization. The even distribution of information by LFP amplitude and 1-4 Hz power compared with the more localized distribution by 100-170 Hz power and spikes suggest that these different neurophysiological signals reflect different underlying processes that distribute information through the motor cortex during reach-to-grasp movements.

Broadband Local Field Potentials Correlate with Spontaneous Fluctuations in Functional Magnetic Resonance Imaging Signals in the Rat Somatosensory Cortex Under Isoflurane Anesthesia

Resting-state functional magnetic resonance imaging (fMRI) is widely used for exploring spontaneous brain activity and large-scale networks; however, the neural processes underlying the observed resting-state fMRI signals are not fully understood. To investigate the neural correlates of spontaneous low-frequency fMRI fluctuations and functional connectivity, we developed a rat model of simultaneous fMRI and multiple-site intracortical neural recordings. This allowed a direct comparison to be made between the spontaneous signals and interhemispheric connectivity measured with the two modalities. Results show that low-frequency blood oxygen level-dependent (BOLD) fluctuations (<0.1 Hz) correlate significantly with slow power modulations (<0.1 Hz) of local field potentials (LFPs) in a broad frequency range (1-100 Hz) under isoflurane anesthesia (1%-1.8%). Peak correlation occurred between neural and hemodynamic activity when the BOLD signal was delayed by ~4 sec relative to the LFP signal. The spatial location and extent of correlation was highly reproducible across studies, with the maximum correlation localized to a small area surrounding the site of microelectrode recording and to the homologous area in the contralateral hemisphere for most rats. Interhemispheric connectivity was calculated using BOLD correlation and band-limited LFP (1-4, 4-8, 8-14, 14-25, 25-40, and 40-100 Hz) coherence. Significant coherence was observed for the slow power changes of all LFP frequency bands as well as in the low-frequency BOLD data. A preliminary investigation of the effect of anesthesia on interhemispheric connectivity indicates that coherence in the high-frequency LFP bands declines with increasing doses of isoflurane, whereas coherence in the low-frequency LFP bands and the BOLD signal increases. These findings suggest that resting-state fMRI signals might be a reflection of broadband LFP power modulation, at least in isoflurane-anesthetized rats.

Phase-of-firing information coding in laminar cortical architecture

We applied recently developed information theory methods [1,2] to the analysis of cortical responses in a large-scale computational model of cat primary visual cortex [3]. These methods quantify the information conveyed by spikes and by local field potentials (LFPs) in a very general way, without ad hoc assumptions about precisely which stimulus features (orientation, direction, etc.) drive the neuronal response. The phase-of-firing information is the extra information obtained by labeling spikes with the value of the LFP phase [2]. In order to gain insight into the information-processing properties of laminar cortical microcircuits, we calculated the spike count information conveyed by firing rates and the phase-of-firing information conveyed by LFPs for each layer of primary visual cortex. We found that there is substantially more information in the phase code compared with the spike rate alone for low LFP frequencies (< 30 Hz). Figure ​Figure11 shows that the information gain for the phase code may reach 80 % in Layer 2/3, while in Layer 4 it reaches only 40 %, compared to the spike count code. These data support the hypothesis that the thalamo-cortical layers, which receive direct sensory input, may rely more on spikes to convey the information, while the cortico-cortical layers with strong recurrent connectivity may use the phase code and LFP signals for information coding. Figure 1 The phase-of-firing information as function of the considered LFP frequency (computed up to 300 Hz). The circles denote the information gain by the phase-of-firing code compared to the spike count code for Layer 4 (A) and for Layer 2/3 (B).

Different LFP frequency bands convey complementary information about the BOLD signal

Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most widely used noninvasive imaging technique for investigating brain activity. However, the BOLD signal is only indirectly coupled to the underlying neural activity and the relationship between the two signals is not fully understood [1]. Recordings in anaesthetized and awake monkeys have shown that hemodynamic responses are strongly related to local field potentials (LFPs) [2,3]. LFPs are thought to represent the input and intracortical processing in a cortical area and are usually separated into different frequency bands that reflect different neural processes [4]. Previous studies have shown that different LFP bands correlate differently with the BOLD signal [3,5,6]. However little is known about which property of the BOLD signal is reflected by each band and whether different bands convey different information about the BOLD signal. To address this question we performed simultaneous recordings of neural activity and BOLD fMRI in early visual areas V1 and V2 in 4 anesthetized monkeys. All measurements were performed with the monkeys sitting in complete darkness while no stimulus was being presented. We computed mutual information between LFP power and BOLD fMRI to determine which frequencies in the LFPs were most informative about the BOLD signal. We found three highly informative bands, namely the alpha band [8-12Hz], the gamma band [40-100Hz] and the [18-35 Hz] “nMod” band that was previously found to be unrelated to visual stimuli and was thus suggested to primarily reflect neuromodulatory input [4]. We found that gamma power was the most informative about BOLD fMRI and reflected well changes in the amplitude of the BOLD signal. In particular, an increase in gamma power above its median value was followed, on average, by an increase in BOLD signal, and the BOLD signal decreased, instead, following a decrease in gamma power below its median. Moreover, we found that gamma and nMod power were complementary, i.e. that by combining nMod power together with gamma power we could extract 30% more information than could be extracted from gamma power alone. We investigated the origin of this complementarity and we found that the power in the nMod band reflected the timing with which changes in BOLD signal occurred following changes in gamma power. Finally, we found that, as suggested by previous theoretical work [7], an increase in alpha power without a change in total LFP power was followed by a decrease in BOLD signal and vice versa. These results indicate that distinct neural processes are reflected differently in the BOLD signal and that, consequently, it may be possible to retrieve information about the different contributions from the recorded BOLD time course.

Spatial reach of the local field potential is frequency dependent

The local field potential (LFP), the low-frequency part of the extracellularly recorded electric potential, is a signal commonly used to study the activity of neural populations. However, it is not clear how local the ‘local’ field potential in fact is, i.e., how large the populations are which contribute to the signal at a given point [1]. The spatial range of LFP will naturally depend on the spatial range of correlations in the neuronal dynamics, which may be different in different frequency bands. However, we expect that also the inherent low-pass filtering in the neuronal cables, which stems from the properties of the cable equation [2], will contribute to the frequency dependence of the spatial range of the LFP. To investigate how dendritic filtering affects the spatial range of different frequency components of the LFP we simulated a population of morphologically reconstructed neurons homogeneously distributed within a disc of radius R = 1mm (Figure ​(Figure1A).1A). The cells were driven by noise input of varying degree of correlation. We calculated the LFP at the center of the disc. We defined the reach of the LFP as radius r 100 Hz) components can be even four times smaller than the reach of the low-frequency (< 50 Hz) components (Figure ​(Figure1B1B). Figure 1 A. Model setup, r (reach) and R defined in text. B. The reach r as a function of LFP frequency for a population of layer 5 pyramidal neurons stimulated basally. The input correlation is 10% (each pair of neurons shares on average 10% of synaptic currents), ... The explanation of the effect is the following: consider a neuron stimulated with a sinusoidal input current (frequency f) at one point in the dendritic tree. The distribution of the return currents depends on f, with typically more local return currents for large f, see [3]. Therefore the ‘critical distance’, where the dipole approximation of the LFP becomes valid, decreases with f. We derived a simplified population model that accounts for the frequency dependence of the ‘critical distance’. The model can be studied analytically and explains the frequency dependence of the LFP reach for both uncorrelated and correlated synaptic input.

Does the information in the phase of low frequency LFP reflect the low frequency envelope of local spike rates?

Recently, it has been shown that when the timing of spikes is measured relative to the phase of the cortical local field potentials (LFP), spikes can carry substantial more information about an external stimulus [1]. Experimental studies in sensory cortices of macaque have shown that the extra information obtained with such phase-of-firing codes above that in the firing rate alone ranges from 55% in primary visual cortex [1] to more than 100% in primary auditory cortex [2]. Here, we use a mathematical model that relates local spike trains and the resulting LFP, to explain the emergence of the phase-of-firing codes in cortex. The model is based on the one proposed in [3] and incorporates two types of integration over the spiking activity: i) a time convolution that results from the filtering properties of neural structures [4], which embeds history effects in LFP from past spiking activity, and ii) an integration step over the activity of neurons in the neighbourhood of the measuring electrode. When the spikes recorded from macaque primary visual cortex were used to synthesize the LFP, the model could reproduce the phase-of-firing information found using the real LFP, as shown in Figure ​Figure1.1. This suggests that an important component of phase-of-firing information originates from the surrounding neural population and past spiking activity. The next question that arises is what is the relative contribution of the neuron population size and the length of the firing rate history embedded in the LFP. We are currently investigating this question by parametrically varying both the population size and time integration ranges in generating the synthetic LFP. Figure 1 Comparison of the phase-of-firing information using synthetic and real LFP. The original data corresponds to LFP and spiking activity from 78 recordings channels in macaque V1, obtained while the animals were presented a movie [1]. A. Information in the ...

BOLD responses to different temporal frequency stimuli in the lateral geniculate nucleus and visual cortex: insights into the neural basis of fMRI.

The neural basis of the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) remains largely unknown after decades of research. To investigate this issue, the unique property of the temporal frequency tuning that could separate neural input and output in the primary visual cortex was used as a model. During moving grating stimuli of 1, 2, 10 and 20Hz temporal frequencies, we measured 9.4-T BOLD fMRI responses simultaneously in the primary visual cortex of area 17 (A17) and area 18 (A18), and the lateral geniculate nucleus (LGN) of isoflurane-anesthetized cat. Our results showed that preferred temporal frequencies of the BOLD responses for A17, A18 and LGN were 3.1Hz, 4.5Hz and 6.0Hz, respectively, which were comparable to the previously reported electrophysiological data. Additionally, the difference of BOLD response onset time between LGN and A17 was 0.5s, which is 18 times larger than the difference of neural activity onset time between these areas. We then compared the frequency-dependent BOLD fMRI response of A17 with tissue partial pressure of oxygen (pO(2)) and electrophysiological data of the same animal model reported by Viswanathan and Freeman (Nature Neuroscience, 2007). The BOLD tuning curve resembled the low frequency band (<12Hz) of local field potential (LFP) tuning curve rather than spiking activity, gamma band (25-90Hz) of LFP, and tissue pO(2) tuning curves, suggesting that the BOLD fMRI signal relates closer to low frequency LFP.

Cortical dynamics during naturalistic sensory stimulations: Experiments and models

We report the results of our experimental and theoretical investigations of the neural response dynamics in primary visual cortex (V1) during naturalistic visual stimulation. We recorded Local Field Potentials (LFPs) and spiking activity from V1 of anaesthetized macaques during binocular presentation of Hollywood color movies. We analyzed these recordings with information theoretic methods, and found that visual information was encoded mainly by two bands of LFP responses: the network fluctuations measured by the phase and power of low-frequency (less than 12 Hz) LFPs; and fast gamma-range (50–100 Hz) oscillations. Both the power and phase of low frequency LFPs carried information largely complementary to that carried by spikes, whereas gamma range oscillations carried information largely redundant to that of spikes. To interpret these results within a quantitative theoretical framework, we then simulated a sparsely connected recurrent network of excitatory and inhibitory neurons receiving slowly varying naturalistic inputs, and we determined how the LFPs generated by the network encoded information about the inputs. We found that this simulated recurrent network reproduced well the experimentally observed dependency of LFP information upon frequency. This network encoded the overall strength of the input into the power of gamma-range oscillations generated by inhibitory–excitatory neural interactions, and encoded slow variations in the input by entraining the network LFP at the corresponding frequency. This dynamical behavior accounted quantitatively for the independent information carried by high and low frequency LFPs, and for the experimentally observed cross-frequency coupling between phase of slow LFPs and the power of gamma LFPs. We also present new results showing that the model’s dynamics also accounted for the extra visual information that the low-frequency LFP phase of spike firing carries beyond that carried by spike rates. Overall, our results suggest biological mechanisms by which cortex can multiplex information about naturalistic sensory environments.

FMRFamide regulates oscillatory activity of the olfactory center in the slug

In the olfactory center of terrestrial animals, changes in the oscillatory frequency of the local field potential (LFP) are thought to be involved in olfaction-based behavior and olfactory memory. The terrestrial slug Limax has a highly developed olfactory center, the procerebrum, in which the LFP spontaneously oscillates. Although changes in the oscillatory frequency are thought to correspond to the preference for specific odors, our knowledge about the mechanism of this frequency regulation is limited. To clarify the mechanism of the bidirectional frequency changes in the procerebrum, we focused on the neuropeptide Phe-Met-Arg-Phe-NH₂ (FMRFamide), which is known to have neuromodulatory functions in invertebrate nervous systems. Application of FMRFamide decreased the oscillatory frequency via G-protein-mediated cascades. Immunohistochemistry showed that FMRFamide-like-immunoreactive neuronal cell bodies are located in the cell mass layer of the procerebrum, projecting their neurites to the neuropile layers. The procerebrum was shown to also receive innervation from other regions of the cerebral ganglion. Furthermore, according to their morphological and projection characteristics, FMRFamide-containing neurons belong to the subpopulations of both bursting and nonbursting neurons in the procerebrum. The mRNA splice variant encoding multiple copies of canonical FMRFamide was specifically expressed in the procerebrum. Taking into account previous results showing that serotonin increases the oscillatory frequency, our results indicate that FMRFamide and serotonin both regulate the LFP frequency but in exactly the opposite direction in the olfactory center of the terrestrial slug.

Do Electrode Properties Create a Problem in Interpreting Local Field Potential Recordings?

Local field potential (LFP) recordings within the brain have become an important tool used by neuroscientists to make inferences about the activity of a population of cells near an electrode. Each passing year analysis of LFPs in neuroscience seems to bring important new insights on the possible workings of networks in the brain to produce behavior (Buschman and Miller 2007; Canolty et al. 2006; Gregoriou et al. 2009; Liu and Newsome 2006; Lubenov and Siapas 2009; Pesaran et al. 2008; Womelsdorf et al. 2006). Indeed LFPs have become a near-ubiquitous tool in neurophysiology seemingly in use anywhere extracellular spikes are also recorded.

Thalamic single‐unit and local field potential activity in Tourette syndrome

Deep brain stimulation (DBS) of the ventralis oralis (VO) complex of the thalamus improves tics in patients with Tourette syndrome (TS). To neurophysiologically describe the VO complex we recorded, in seven patients with TS undergoing DBS electrode implantation, single-unit activity during surgery and local field potentials (LFPs) a few days after surgery. Single unit recordings showed that the VO complex is characterized by a localized pattern of bursting neuronal activity. LFP spectra demonstrated that VO of TS patients has a prominent oscillatory activity at low frequencies (2-7 Hz) and in the alpha-band (8-13 Hz), and a virtually absent beta activity. In each patient, the main LFP frequency significantly correlated with single-unit interburst frequency. In conclusion, we observed an oscillatory bursting activity in the VO as target region in patients with severe TS undergoing DBS surgery.

Pattern Motion Selectivity of Spiking Outputs and Local Field Potentials in Macaque Visual Cortex

The dorsal pathway of the primate visual cortex is involved in the processing of motion signals that are useful for perception and behavior. Along this pathway, motion information is first measured by the primary visual cortex (V1), which sends specialized projections to extrastriate regions such as the middle temporal area (MT). Previous work with plaid stimuli has shown that most V1 neurons respond to the individual components of moving stimuli, whereas some MT neurons are capable of estimating the global motion of the pattern. In this work, we show that the majority of neurons in the medial superior temporal area (MST), which receives input from MT, have this pattern-selective property. Interestingly, the local field potentials (LFPs) measured simultaneously with the spikes often exhibit properties similar to that of the presumptive feedforward input to each area: in the high-gamma frequency band, the LFPs in MST are as component selective as the spiking outputs of MT, and MT LFPs have plaid responses that are similar to the spiking outputs of V1. In the lower LFP frequency bands (beta and low gamma), component selectivity is very common, and pattern selectivity is almost entirely absent in both MT and MST. Together, these results suggest a surprisingly strong link between the sensory tuning of cortical LFPs and afferent inputs, with important implications for the interpretation of imaging studies and for models of cortical function.